This paper assesses the impact of access to credit from rural and community banks (RCBs) on the technical efficiency of smallholder cassava farmers in Ghana. The study employed the stochastic frontier, and endogenous switching regression models to estimate the technical efficiency, and the impact of RCB credit access, respectively, on a randomly selected sample of 300 smallholder cassava farmers in the Fanteakwa District of Ghana. Results suggest that cassava farmers in the District are 70.5 percent technically efficient implying that cassava yield levels could be increased further by 29.5 percent without changing the current levels of inputs. The results further reveal that the gender of the household head, access to extension services, membership in farmer organizations, and proximity to the bank are the major factors that positively influence farmers to access credit from RCBs. On average, farmers who accessed credit from RCBs have significantly higher technical efficiencies than farmers who did not access, suggesting that access to credit from RCBs positively impacts the technical efficiency of smallholder cassava farmers. Technical efficiency, Credit access, Rural and community banks, Endogenous switching regression, Stochastic frontier model. In 2013, approximately 276 million metric tonnes (MT) were recorded, out of which Africa accounted for approximately 158 million MT, representing about 57 percent of the world's cassava production in 2013 .In Ghana, the crop contributes 22 percent to the country's agricultural gross domestic product (AGDP). It is consumed in all the sixteen administrative regions of the country, thereby serving as an important food security crop . BetweenAccording to Several studies have investigated the factors influencing smallholder farmers' demand for, and use of formal credit. Conclusions drawn from most of these studies are that smallholder farmers' socioeconomic, demographic, and farm-level characteristics influence their demand for, and use of formal credit . Other sAgainst this backdrop, this present study assesses the impact of access to credit from RCBs on the technical efficiency of smallholder cassava farmers in rural Ghana. The study, accordingly, has two specific objectives: (1). to estimate the technical efficiency of smallholder cassava farmers in rural Ghana, and; (2) to measure the impact access to RCB credit has on the technical efficiency of the smallholder cassava farmers in rural Ghana. This study is an attempt to provide new information and bridge the gap in knowledge on rural credit access and its link with technical efficiency; and at the same time provide evidence that will aid policymakers across the sub-region, in formulating policies specific to credit access and enhancements in the efficiency of cassava farmers.The rest of the paper is organized as follows: Section 2Access to credit may not directly impact productivity. However, it may indirectly impact productivity through the positive effects on smallholder farmers’ adoption of agricultural technologies . It may Empirically, the relationship between agricultural credit access and technical efficiency has been studied extensively through different approaches with different underlying assumptions. For instance, in Pakistan, In the Sub-region, In Ghana, We observe that, the studies reviewed employed either one of two approaches. On the one hand, is the stochastic frontier or the DEA methods. In the stochastic frontier approach, access to credit is treated as a covariate in the inefficiency model. In the DEA method, access to credit is observed as one of the factors of production. On the other hand, are studies that employed the PSM technique. Although this method allows a comparison between farmers who accessed credit and farmers who did not access it, the approach faces the issue of selection bias . This cu33.1This study was carried out in the Fanteakwa District of the Eastern region of Ghana see . The disData was collected on farmers’ socio-economic and farm-specific characteristics such as age, gender, education, household size, farm size, output, input quantities, extension access, membership in farmer-based groups, off-farm income, and experience in cassava cultivation. The semi-structured interviews were conducted with the help of agricultural extension agents who served as enumerators. We obtained informed consent from each of the farmers before their interview.3.23.2.1This approach was proposed independently by  from Ui . From thFollowing  (εiλ/σ) .The production function can follow either the Cobb-Douglas functional form or the translog functional form. The translog functional form is generally specified as :(3)lnY=β3.2.1.1ith farmer, i.e., labor, cassava stem cuttings (seed), land, herbicides and pesticides. All the quantities were standardized by dividing each quantity by its respective mean and then log-transformed.In this study, five factors of production were considered. Thus, the production function is empirically specified as:lth farm-level, socio-economic and institutional factor hypothesized to influence the ith farmer's technical inefficiency Eε1aTE1=X1βiIn this study, we employed the FIML approach proposed by 3.2.2.1Actual outcomes refer to the expected technical efficiency of farmers who accessed credit, and counterfactual outcomes explain the expected technical efficiency of farmers who accessed RCB credit had they not accessed. The conditional expectations for the different outcomes are specified below as:Eqs. The average treatment effect on the treated (ATET) is the difference between the actual outcome for accessors and the counterfactual outcome for non-accessors that is a minus (The average treatment effect on the untreated (ATEU) is calculated in the same light. It is the difference between the actual expected outcomes for non-accessors and the counterfactual outcome for accessors (that is (10d) minus (10b)). That is the difference between the technical efficiency of accessors had they not accessed; and the technical efficiency of non-accessors for not accessing. It is expressed as:The next parameter to calculate is the base heterogeneity effect (BHE). It is the difference between the ATET and the ATEU . It is s44.1The above statistics, however, do not depict the exact impact of access to credit on the technical efficiency of cassava farmers in the Fanteakwa District. Conclusions on the impact of access to RCB credit on technical efficiency based on the above differences will be biased. Policy recommendations should, therefore, not be based on them.4.24.2.1The results of the maximum likelihood estimation of the translog production function  are presdf = 20), significant at the 1 percent. This suggests that the model wholly fits the data better than a model with no predictors. The coefficients of the first-order derivatives of the translog production function are interpreted as the partial output elasticities. The estimated first-order coefficients are positive and imply that the monotonicity condition of a rational production function is satisfied (lnFarm) and cassava stem cuttings (lnSeed) are all positive and significant at 1 percent. The coefficient for labor (lnlabor) is significant at 5 percent and that of herbicides (lnHerbicides) at 10 percent. The first order coefficient of pesticides (lnPesticides) is statistically insignificant  suggest that the current level of labor is not optimal and that the MPP of labor continues to rise with every increase in the quantities of labor. Similarly, the estimated coefficient of the squared of seed (lnSeed2 = 0.080) and the squared of herbicides (lnHerb2 = 1.123) suggests that increases in the current quantity of stem cuttings, and herbicides will lead to increases in their respective MPP, holding the quantities of the other factors unchanged. The negative coefficients point to the fact that the current level of the variable factor is above the maximum level. Therefore, any further increase in the levels of the inputs, holding other things constant, will cause the MPP to fall, thereby leading to detrimental effects on yields.The coefficients of the squared terms of the inputs represent the second-order derivatives. A positive coefficient of the squared term suggests that the marginal physical product (MPP) would increase with additional units of the variable input and vice versa,  paribus . Thus, tceteris paribus. The interaction between pesticides and labor also produced a positive coefficient, which implies that labor and pesticides are complements. The interaction between farmland and seed also generated a positive coefficient suggesting that farmland and cassava stem cuttings are complements. Seed and labor, however, had a negative coefficient. It implies that any simultaneous increases in the quantities of stem cuttings and labor will have reducing effects on yields, given that the levels of the other inputs remain constant.The coefficient of the cross terms (the interaction terms) suggests whether the interacted inputs are complements or substitutes to each other . In this4.2.2The minimum and maximum technical efficiencies (TE) recorded were 19.1 percent and 99.4 percent, respectively, with the average TE score being 70.5 percent. About 33 percent of the farmers recorded technical efficiencies in the range of 90–99 percent. The TE score range of 50–59 percent recorded the least number of farmers (5 percent of the farmers). 4.2.3p < 0.1), suggesting that cassava farmers in the Feyiase community are more technically efficient. These differences could be ascribed to differences in farm management practices as well as climatic and edaphic factors. The social capital variable (FBO membership) met the a priori expectation of a negative effect on technical inefficiency, although it is statistically insignificant. The negative coefficient means membership in farmer-based groups helps to improve the TE of the farmers.The coefficients for Ahomahomasu and Begoro were both positive and significant at 5 percent and 10 percent, respectively, using Obuoho as the reference community. The results imply that cassava farmers in Ahomahomasu and Begoro are less technically efficient than those in Obuoho. The coefficient for Feyiase produced a negative coefficient estimate (at 4.34.3.1savings) met the a priori expectation of a positive effect on farmers’ decision to access credit from the RCBs and is statistically significant (Gender) and educational status of the household head (Education) met their respective a priori expectations but are statistically insignificant. However, contrary to our a priori expectation, the coefficient of farm size is negative and significant at 10 percent. It means that the larger the farm, the lesser likelihood that a smallholder cassava farmer will access credit from RCBs.The result from the first stage of the ESR shows that among the demographic and socioeconomic factors that were included in the model, having a savings account with the bank (nificant . Gender a priori expectation of a positive effect on farmers' access to credit from RCBs (p < 0.01). The implication is that farmers who access agricultural extension agents are more likely to access credit from RCBs in the District. Proximity to the bank also met the a priori expectation of a negative effect on farmers' access to credit from RCBs. It suggests that farmers who stay farther away from the RCB are less likely to access it. In other words, living closer to the bank increases farmers’ likelihood of going for credit from RCB. Membership in farmer organizations also met the a priori expectation of a positive effect on RCB credit access. The positive coefficient means that a smallholder cassava farmer will be more inclined to access credit from RCBs if the farmer joins a farmer group.Among the institutional factors that were hypothesized to influence farmers' decision to access credit from RCBs, access to agricultural extension services met the 4.3.2The expected average TE of smallholder cassava farmers who accessed RCB credit (a), is 0.718 and that of non-accessors (d) is 0.707 . Cells  program and the Fertilizer Subsidy Program (GFSP), can be restructured to include cassava as one of the priority crops. Sustainable investments made on agricultural technologies will lead to improvements in the TE of cassava farmers. This result is corroborated by The negative coefficient of formal education in the inefficiency model suggests that formal education reduces farmers’ technical inefficiency . The implication is that farmers who have attained at least primary formal education tend to be relatively more efficient in their resource use. This result may be attributed to the fact that improvement in human capital is an enabling factor for efficient resource utilization among rural households. This finding is in line with a priori expectation was that farmers who stay closer to their farmlands would have easy and timely access to the farm. Staying closer to the farmland makes it easier for the farmer to carry out the best farm management practices since the farmer incurs little or no cost when accessing the farm. This result contradicts The negative coefficient of proximity to farmland implies that farmers who stay farther from their farmlands are less technically inefficient  vis-a-vis farmers who live closer to their farmlands. This finding is surprising as the Agricultural extension agents are a vital source of agricultural information for farmers in the rural parts of Ghana. The negative coefficient of extension access in this study implies that smallholder cassava farmers with access to agricultural extension services are less technically inefficient collated with farmers without access to extension services. This finding is in line with that of Although the social capital variable, FBO membership, was statistically insignificant for farmers in the Fanteakwa district, the negative coefficient supports the assertion that farmers who belong to farmer groups get the chance to learn from their fellow farmers. They learn about the latest output-enhancing technology and the best farm management practices, among other things, thereby making them less technically inefficient compared to farmers who are not members. Therefore, visible support from the government to farmer organizations will encourage more farmers to join such groups. The long-run effect of this is improvements in farmers’ resource use efficiency. This finding is supported by 5.2The statistically significant rho from the ESR model in From the results, farmers' likelihood of accessing credit from RCBs is significantly high if they own savings accounts with the bank. This finding can be attributed to the fact that most banks in Ghana run the savings-before-credit policy, which requires borrowers to have active savings account with the bank before they can be deemed eligible for credit. This result is consistent with , who alsproximity to the bank may be explained by the fact that the banks are situated in the district capital. Moreover, most of the farmers are living in communities outside the district capital. Therefore, farmers who stay farther away from the district capital are less likely to hear about the credit programs and access. Farmers who hear about the credit programs may be less motivated to access them due to the high transaction costs. The relationship observed in this study between credit access and proximity to credit source is consistent with Studies have found that transaction cost influences the adoption of agricultural technology . The neg6This paper assessed the impact of access to RCB credit on TE using farm level and household level cross-sectional data for the 2017/2018 cassava production season in the Fanteakwa district in Ghana. The stochastic frontier model was used to estimate the TE score of the cassava farmers in the District. The ESR model was used to control for self-selection into RCB credit access and generate consistent actual and counterfactual outcomes.The results suggest that cassava farmers in the Fanteakwa district are 70.5 percent technically efficient. It implies that cassava output could be increased further by 29.5 percent by improving farmers’ efficiency. Through the Planting for Food and Jobs (PFJ) program, the government of Ghana can make available to the farmers improved cassava seeds. Also, the results indicate that the TE of farmers is knowledge-based, given the effects of extension access and membership in farmer-based groups on their resource use. It calls for policies that strengthen the farmer-based organizations and enhances the mode and scope of extension service delivery, particularly in the rural parts of the country. The gender of the household head, extension access, land ownership, and income from off-farm economic activities are the major factors that positively influence farmers to access credit from RCBs. On average, farmers who accessed credit from RCBs have higher technical efficiencies than farmers who did not. It points to the need for a more holistic information dissemination approach to increase awareness and adoption of RCB credit programs among rural-based smallholder farmers.This study assessed the impact of RCBs on TE only at the district level. Future studies may conduct a country-level analysis to assess the overall impact of RCBs in the crop production sub-sector of Ghana. Future studies may also investigate the rates of adoption of RCB credit programs among smallholder farmers. Also, a country-level comparison of the technical efficiencies of cassava producers in SSA will shed more light on the technological differences in cassava production across the sub-region.Arnold Missiame, Rose A. Nyikal, Patrick Irungu: Conceived and designed the experiments; performed the experiments; analyzed and interpreted the data; contributed reagents, materials, analysis tools or data; wrote the paper.10.13039/501100016991African Economic Research Consortium (AERC).This work was supported by Data will be made available on request.The authors declare no conflict of interest.No additional information is available for this paper.


Localisation of geometric features like holes, edges, slots, etc. is vital to robotic planning in industrial automation settings. Low-cost 3D scanners are crucial in terms of improving accessibility, but pose a practical challenge to feature localisation because of poorer resolution and consequently affect robotic planning. In this work, we address the possibility of enhancing the quality of a 3D scan by a manual ’touch-up’ of task-relevant features, to ensure their automatic detection prior to automation. We propose a framework whereby the operator (i) has access to both the actual work-piece and its 3D scan; (ii) evaluates the missing salient features from the scan; (iii) uses a haptic stylus to physically interact with the actual work-piece, around such specific features; (iv) interactively updates the scan using the position and force information from the haptic stylus. The contribution of this work is the use of haptic mismatch for geometric update. Specifically, the geometry from the 3D scan is used to predict haptic feedback at a point on the work-piece surface. The haptic mismatch is derived as a measure of error between this prediction and the real interaction forces from physical contact at that point on the work-piece. The geometric update is driven until the haptic mismatch is minimised. Convergence of the proposed algorithm is first numerically verified on an analytical surface with simulated physical interaction. Error analysis of the surface position and orientations were also plotted. Experiments were conducted using a motion capture system providing sub-mm accuracy in position and a 6 axis F/T sensor. Missing features are successfully detected after the update of the scan using the proposed method in an experiment. Manufacturing automation makes use of essential geometric features in object reasoning for robot planning. Features are defined as a combination of a group of relative geometric entities like a face, edge, loop or a vertex . Examplemm scale accuracy, the depth sensing is slightly rougher. However, the proposed framework allows the use of fine torque sensing also available on-board to improve the quality of depth sensing by refining missing features. One of the challenges, however, is represented by the typically poorer resolution of such low-cost 3D vision systems, compared with higher-end alternatives. Although high-quality portable scanners [Current practice makes use of a large spectrum of 3D vision technologies to scan work-pieces to derive 3D kinematic models for robot motion planning. The availability of low-cost 3D vision systems encourages the use of robots in less-structured environments, e.g., workshops. For example, robotic manipulators nowadays are equipped with both position sensing and depth sensing along with torque sensing. Although the position sensing is within the scanners  overcomescanners , solutioscanners . We prophaptic stylus  to physically interact with the actual work-piece, around such specific features; (iv) updates the geometry of the scan in real time using the position and force information from the haptic stylus.The proposed framework has the following workflow where an operator (i) has access to both the actual work-piece and its 3D scan; (ii) evaluates which salient features are missing from the scan; (iii) uses a not address the subsequent robotic motion planning, including calibration problems between robot coordinates, work-piece coordinates and 3D scan coordinates. Furthermore, we shall assume that the operator, based on his/her experience, knows which salient features are needed for a specific task. In this sense, we will not deal with exploration strategies which vary from operator to operator and are task-dependent.This work, however, will only focus on the latter points, i.e., on the way the initial scan is updated, for the purpose of enhancing salient features based on data derived from the physical interaction with work-piece via the haptic stylus. More specifically, in this paper we will haptic mismatch between the actual force, as sensed by the haptic stylus from the interaction with the actual work-piece, and the expected force, which is predicted based on the geometry of the surface and current position of the stylus. It should also be noted that the information used to update the geometry of the surface in this process not only makes use of the 3D position of the stylus end-tip, as typically done in digitization techniques, but also of the 3D forces (magnitude and direction) arising from the actual interaction, hence the name ’haptic’ stylus. In particular, the mesh (from the 3D scan) is updated only in presence of haptic mismatch. The 3D position of the stylus determines where to update the mesh while the 3D forces are used to determine the direction and magnitude of the update. This is in contrast with the other approaches in haptic digitization. Specifically, most methods use the knowledge that there was physical contact at a location as a way to record this location as part of the surface model. This will be detailed in the next section. Some challenges with these methods include thresholding the sensed contact to avoid outlier points, filtering the recorded points and fitting a surface on them. The computational demand in implementing these methods in real time is yet another challenge. The proposed framework overcomes these challenges by using the forward prediction of haptic feedback obtained from the initial 3D scan and then uses the backward haptic feedback to refine it, in real time.As explained in the following sections, we use a discrete mesh as a numerical estimation of the actual work-piece surface. Initialized with a 3D scan, this mesh is continuously updated based on the physical interaction between the work-piece and the haptic stylus held by an operator. The novelty of our work is that the geometric update of the mesh is based on the The rest of the paper is organized as follows. In In this section we review different non-contact and contact based scanning technologies and the trade-offs between them. The challenges in using high end scanners in terms of cost, additional expert skill requirement, lack of mobility and limited scanning space are identified as gaps our framework aims to address. Various available 3D model formats for representing the surfaces are presented. Haptic based scanning methods are surveyed to discuss the framework’s relevance in robotics research. Finally, haptic sculpting literature and its relevance to our work is studied to highlight the novelty in our approach.Manufacturing industries have employed non-contact based 3D scanning technologies such as laser triangulation, structured light, time-of-flight and photogrammetry  in part wrap a plain mesh for synthesising novel views. The proposed work is an analogue to this method but is driven by forces from physical interaction instead of visual features. Specifically, the wrapping of a mesh to conform to absent geometric features using haptic feedback is the haptic analogue we present.Different non-contact sensors capture 3D information and use a variety of output formats. Popular models to visualize the 3D geometry include point clouds, polygonal meshes, Non-uniform rational basis spline (NURBS) surface models, etc. Interconversion between these models is well established. In this work we use Occipital’s structure sensor  which coμm – mm level and understanding material properties like magnetism, elasticity, etc., one of the main drawbacks is the limited mobility and the special requirements like vacuum environments for operation. Coordinate measuring machines (CMMs) have also used physical contact to improve coordinate metrology [Among contact based high resolution scanning methods, one of the early inventions is the Atomic Force Microscopy (AFM) method . This meetrology . Other cetrology  for manuetrology  and reveetrology  by linkietrology ,18,19,20haptic digitization, i.e., the use of active touch to reconstruct the shape of an object for perception of size and position is also an active field of research. Haptic perception caused when people move their hands over an object to decipher smoothness, roughness, curvature, etc. [Haptic exploration. Haptic exploration may be used for feature detection, i.e., of bumps, cracks, ridges, etc. Early approaches to this problem proposed incremental updates of a probabilistic map of the environment for a known smooth surface [Haptic SLAM. Similar implementations using particle filters [Digitization is the process of reverse engineering virtual models of actual objects. Conventional contact based scanning methods for digitization largely targeted at part inspection were discussed in the previous subsection. However, re, etc.  is attri surface  and unkn surface . This is filters  have dem filters  presente filters . Some mo filters , localiz filters , improve filters . Interes filters  used vis filters ,32,33,34 filters .We next study various techniques in sculpting, which is the step that follows digitization of an object.actual haptic feedback, i.e., the force readings recorded during physical interaction with the object and differs from them fundamentally in this sense.Our work draws inspiration from many sculpting techniques already in use for computer aided manufacturing applications. Haptic sculpting constitutes subtracting of materials from digital objects  to modiftarget valueMost of these surveyed approaches focus on extracting the shape of an object or a surface using vision or touch or both. The common applications across them were either (i) object shape estimation/reconstruction/repair or (ii) localization of object by way of reconstruction or acquiring samples of surface points. One commonality among these methods is the use of a sensorized stylus or a tactile array capable of providing end-tip position and contact (on/off) information to perform point-wise virtual mapping using touch information—a binary view of physical touch. Spatial location proaches ,33,34,45proaches ,30, occuproaches ,44 and sproaches ,46,47. Hproaches ,48 do usclay-like manner to actual forces exerted by the human operator on the real object using the haptic stylus.From a computational standpoint, most of the approaches collect touch information as a tuple urrently . By desiHaptic SLAM and haptic sculpting are very similar to the proposed framework in this paper. However, our work differs from them in some fundamental ways. Our key contribution is the idea to use the geometry available from the 3D scan to compute an expected haptic feedback at a point and compare it with actual haptic feedback to derive a haptic mismatch. This in turn is used to drive the update of the 3D scan. In terms of how the virtual model is morphed, most of the surveyed methods assume voxel-based representations of objects or GPIS and involve volume subtraction or updating voxel occupancies and gaussian process regression for mapping the surface of an object. Our methodology morphs the 3D model locally at the surface directly.These works in touch based scanning, haptic digitization, haptic mismatch and define the two ways in which the update of the 3D scan at a local point occurs, i.e., position and orientation corrections.In the following section, we provide an overview of the proposed framework following which we mathematically formulate it. We also formalise the notion of We propose a framework whereby a human operator, after a visual inspection of the initial scan, may proceed to a manual refinement of the scan by means of a haptic stylus used to physically interact with the actual, rigid surface b. Considactual interaction with a surface. Additionally, the position and orientation of the stylus are also to be sampled for real-time rendering purposes.To record the haptic feedback, we use a haptic stylus as shown in actual haptic feedback is recorded in terms of forces and torques. Concurrently, the internal model predicts the expected haptic feedback. The internal model is a haptic rendering of the mesh structure obtained from the 3D scan, i.e., the geometry of the 3D scan is used to compute a prediction of the force feedback to be expected when a tool comes in contact with a surface point. This prediction is then compared with the actual haptic feedback to produce a haptic mismatch. The haptic mismatch coupled with the haptic update rules of the framework correct the surface scan geometry in terms of position and orientation of individual surface points to reflect the real surface’s geometry accurately.Upon surface-tool interaction by the haptic stylus with a real surface an expected haptic feedback, given a position of the haptic stylus. The two types of haptic mismatch for position and orientation correction of a surface point in the scan are presented with qualitative case studies. Briefly, these cases are meant to understand the positional and orientation haptic mismatch for different errors in the surface scan w.r.t the actual surface. For instance, based on whether or not the haptic stylus is penetrating the 3D mesh, an expected haptic feedback—both in terms of magnitude and direction may be computed. This may or may not be non-zero and both these cases may correspond with a zero or non-zero actual haptic feedback sensed. Studying these cases will help understand the working of the proposed framework. Consequently, the mathematical rules to correct the haptic mismatch are summarized and a numerical validation of these rules for one surface point is presented. These rules constitute the aforementioned haptic update. Finally, error metrics are defined and a numerical simulation of the haptic update is carried out in the 3D case to quantitatively characterize the framework’s performance.To model surfaces in 3D we use triangular meshes because they allow to work with a locality in the model while keeping the rest of the regions of the mesh intact. This allows for efficient implementations. To mathematically describe the mesh obtained from the scan of the real surface in a reference coordinate system, we define state variables. These variables will be used to define the interaction function used to calculate the estimate of the surface geometry, we denote the state variables related to the scan with the superscript The basic idea of the framework is to adapt (expected force (haptic feedback), it is useful to define a signed distance h as:expected haptic feedback The mesh represents the actual surface to the best of the sensor’s accuracy and as such, it will be used to predict interaction. In other words, if the haptic stylus was to penetrate a given face of the mesh, we would expect an interaction force perpendicular to the face normal . In order to calculate the h, i.e., when there is no penetration. The interaction force function The nature of h. The lower the depth tolerance, the stiffer the function, i.e., for lower displacements, a higher contact force is estimated. Furthermore, the interaction force function allows a choice over the range of forces  and its face normal (orientation information). These are the two vector variables that will be adapted to correct the haptic mismatch using the The purpose of the position correction of the face barycenter on the mesh is to ensure that it lies on the plane described by the actual point of contact and the surface normal there. To this end we define the linear error in forces by:moving the face of interest along its normal until The correction of linear haptic mismatch is essentially a linear translation of the face of interest on the scan along its normal direction In the case studies in Once the barycenter of the face (from the mesh) lies on the actual surface, the next step is to ensure the face normal matches with the surface normal. We define a rotational error in forces by:expected haptic feedback and the actual haptic feedback match in terms of their vector direction. In other words, the unit vector along haptic mismatch is corrected through the position correction. Note that the interaction force at any point on the surface is along the surface normal there. Hence, the cross product of haptic update.The cross product rule may be viewed as analogous to inducing a moment on the face to rotate it until rotational equilibrium is reached. This would occur when both the haptic mismatch may be summarised as simply a positional correction for Cases 2 and 3 and a positional and orientation correction for Case 4. This may also be noticed from The overall correction of haptic update rules qualitatively described in the previous subsections is presented next. The state variables are (i) the barycentre haptic mismatch is done by solving the following differential equations defined on the state variables:A summary of the face dynamics of the scan. The variables in these update rules are listed out in haptic update.Together, Equations  and 7)  7) consthaptic stylus dynamics combined with face dynamics are defined as coupled dynamics of the haptic update. The set of differential equations from the coupled dynamics are solved using standard numerical solvers and error functions are evaluated over the results to quantify the performance of the framework. The application of the framework in the 3D case is also presented with numerical simulation results for the same.In this section, to numerically validate the proposed framework, we define the haptic stylus dynamics, i.e., dynamical behaviour of the haptic stylus position For simulation purposes we define the constraints on the haptic stylus motion, haptic update simulation resulting in the haptic stylus (described by its position The forces influence the Equation .(10)StyNote that the reaction force of the actual surface on the haptic stylus denoted by The coupled dynamics are denoted by: quations , 7) and and(11)dquations .medium order method with ode45 solver in MATLAB are shown in haptic update for one surface point. We define dynamic equilibrium for the state variables when they are unchanging. In practice, we need only run the ode45 solver over the differential equations for a finite time  and andb*, nquations  using thdistance error and normal deviation respectively. The behaviour of the framework based on these is characterized in haptic update rule in Equation  with respect to the actual surface. The error metrics are defined as deviations of Equation   respectively, are calculated using haptic rendering techniques, using the surface penetration based contact model as shown in Both the meshes are referenced in the same global coordinate system. To check whether or not a surface point on haptic update rules to refine the mesh. We use the 1-neighbourhood Laplacian surface editing for mesh deformation. The mechanism of haptic update may be observed along the path of the red bead moving on the mesh* in mesh* is morphed to conform to the actual surface modelled by meshA.The framework is initialized to these conditions and the state variables  the deviation in face normals and the distance between face planes decreased consistently. We can design this process to be faster by choosing the viscous coefficients of the haptic update   hatptic visual inspection of the features deemed important to the process and enables interactive usage of the proposed framework. The 3D scan is updated based on the perceived haptic mismatch until it is minimised. The 3D scans of the two objects with more than 4 holes missing, and smoothed out edges and slots were refined using the proposed framework. All the missing features were successfully refined and feature detection was performed in post-processing.The procedure followed to perform the geometric refinement of features is explained in Quanser QPIDe Terminal Board and analog data from the ATI Mini40 6-axis F/T sensor  in the loadcell frame of reference {l} . Note that for calculating the haptic mismatch, these forces must be converted and expressed in space frame The experiment was conducted using three Desktop machines see b: QuansePTI Phoenix Visualeyez II VZ4000V Motion Capture system to track the 3D position of Motion Capture LEDs (markers) mounted on the hand-held haptic stylus. The haptic stylus pose was calculated from this and a UDP socket was written to communicate this information  Clustering over the meshes to obtain control points, (v) Point set registration on the control points to align the refined-mesh* and CAD-mesh  Pinterest d, (iii) CAD-mesh a and  refined-mesh* with CAD as shown in initial-mesh* and refined-mesh*.Point set registration was performed to align the two meshes (i) refined-mesh*.Post alignment, the final step of evaluating the performance of our framework is to check if the geometric differences between the CAD and the refined scan were indeed less than those with unrefined scan. It may be observed in Although the error does not vanish owing to human error, calibration error, etc. in the experiments shown above, the ability to perform haptic exploration of the workspace and update the robot’s knowledge is of critical importance in applications where the robot needs to physically interact with features like holes, edges, corners that may be absent in the 3D scan of the environment.actual haptic feedback to perform virtual sculpting. The idea of haptic mismatch was introduced and defined as the error between the actual haptic feedback and the expected haptic feedback. The expected haptic feedback is computed using the geometry available from the 3D scan and the goal of the framework is to refine the 3D scan by physically interacting with the actual object via the haptic stylus to minimize haptic mismatch. The proposed framework was experimentally validated. In particular, an object was scanned and the haptic stylus was used to refine features like holes and slots which were missing in the initial 3D scan. All the missing features were successfully refined in the 3D scan using the framework.The proposed framework is a pre-manufacturing step for an operator to refine missing geometric features in a 3D scan before automated manufacturing can take place. The novelty of this framework is to use where it was updated, this is left entirely to the operator . As part of future work, one possibility is to introduce this memory in the form of texturing or other dynamic properties of the mesh, e.g., so that previously updated areas are made less malleable. Other limitations include sensitivity to noise and position calibration of haptic stylus to scan. This can be also addressed in the future with a probabilistic version of this framework. The comparability of the tool shape with the size and shape of the targeted feature for refinement is important for effective usage of this framework. Additionally, the surface friction will also play a role in certain tasks—in this work we do not include this as a property of the mesh.Deploying cost-effective robotic systems in low volume high mix contexts, would face challenges in 3D perception. The significance of this work is in bridging this gap by providing a framework capable of harnessing position and force feedback to improve 3D perception and make automation more accessible. One of the limitations of our work is that the mesh updating does not keep memory of


The implementation of emission-computed tomography (ECT), including positron emission tomography and single-photon emission-computed tomography, has been an important research topic in recent years and is of significant and practical importance. However, the slow rate of convergence and the computational complexity have severely impeded the efficient implementation of iterative reconstruction. By combining the maximum-likelihood expectation maximization (MLEM) iteratively along with the Beltrami filter, this paper proposes a new approach to reformulate the MLEM algorithm. Beltrami filtering is applied to an image obtained using the MLEM algorithm for each iteration. The role of Beltrami filtering is to remove mainly out-of-focus slice blurs, which are artifacts present in most existing images. To improve the quality of an image reconstructed using MLEM, the Beltrami filter employs similar structures, which in turn reduce the number of errors in the reconstructed image. Numerical image reconstruction tomography experiments have demonstrated the performance capability of the proposed algorithm in terms of an increase in signal-to-noise ratio (SNR) and the recovery of fine details that can be hidden in the data. The SNR and visual inspections of the reconstructed images are significantly improved compared to those of a standard MLEM. We conclude that the proposed algorithm provides an edge-preserving image reconstruction and substantially suppress noise and edge artifacts. Image reconstruction is a type of inverse problem , and as By using iterative image reconstruction algorithms, it is possible for these inadequacies to be avoided ,13,14,15Within ECT, the most commonly used iterative algorithms are the maximum-likelihood expectation maximization (MLEM) ,17,18 alBased on the above understanding of past works ,22,23,24In b number of detectors and we measure at a angles, then the number of elements of projection data vector, N is the number of detectors in the ring, then x with I elements, where ith pixel, In the MLEM algorithm, the measured projection datasets play a significant role. In a SPECT scanner, the size of the projection data depends on both the number of detectors in the camera strip and the number of angles. If the camera contains on model . Therefoi to be detected by detector j. It is possible to calculate the expected value of the projection data depending on the system matrix using the following formula [We know that a system matrix represents the probability distribution of the projection data. Hence, elements of system matrix  formula ,18,29:(1Because The likelihood function quations  and 2)  L(x) indL are the Kuhn-Tucker (TK) conditions [It can be seen using the first and second derivatives of the log-likelihood function that the matrix of second derivatives is negative semi-define, and that  concave . As a renditions :(4)0=x.Analyzing Equation , the MLETo identify a suitable resolution for ECT image reconstruction, the iterative filtering structure of the MLEM method is used as a result of uniting an enhanced image-filtering methodology into the existing MLEM iteration strategy, specifically, an existing iterative filtering strategy that endorses and supports the MLEM image quality during the iterative development. As a result of upholding the key aspects, the vast proportion of dimensions functioning within the image are entrenched into an improved dimension that enables the use of the influential disparity geometry operatives .An inventive geometric dispersal flow method is the Beltrami flow, which has the objective of lowering the image area manifold and motivating the flow in the direction of a minor surface resolution, while simultaneously upholding the edges.The Beltrami framework, is based on a nonlinear flow that was applied as an edge-preserving denoising and deblurring algorithm for signals and especially multi-channel images.x with consideration of time t. In addition, x with consideration of The manner in which the Beltrami flow is communicated is shown below :(7)xt=1gg is used to signify the determinant for the initial essential form of the surface, which is represented as g originates from a prompted metric for the Euclidean g is to deliver the measurement of the area increase amid surface domain S and image domain x, and consequently is a substantial element in encouraging the flow in the direction of the surface with the smallest surface area.Lastly, imension  . MLEM algorithm was combined with Beltrami image flow filtering to reduce the noise and unwanted artifacts with preserving the edge information which is the filtered MLEM algorithm for the unpenalized problem by including an additional filtering step in each iteration. As is well known, the MLEM algorithm is an iterative approach that increases the likelihood of the estimated image in each iteration. Our proposal is to introduce a filtering step for each iteration through which the current estimated image is smoothed in a suitable manner.In this section, we propose a new scheme called filtered MLEM  and the relative norm errors (f-MLEM algorithms of Huffman Brain Phantom in terms of performance parameters using different number of iterations. f-MLEM algorithm performs better even at limited number of iterations.To more illustrate the benefits of our algorithm, we provide f-MLEM algorithm outperforms the conventional MLEM algorithm. From the above observations, we can conclude that the proposed algorithm provides an edge-preserving image reconstruction and substantially suppress noise and edge artifacts present after a small number of iterations. It also extends the conventional MLEM algorithm in reconstructions from noisy random projections with a small number of iterations.In all the visual-displays, the quality measurements and line plots demonstrate that the proposed However, despite all these advantages, this new technology is still suffering from the drawback. One drawback of these proposed algorithms compared to conventional analytical solutions is their high computational complexity. However, due to the continuous improvement of computer technologies, effective programming methods and intelligent implementation techniques and other aspects of modeling, this deficiency has been partially surmounted.f-MLEM algorithm delivers a substantial enhancement in terms of reconstructed image standards and preciseness in comparison to a regular MLEM algorithm. Furthermore, the application and execution of the projected f-MLEM is extremely straightforward and avoids disrupting the physical models it defines. This study demonstrated that the proposed algorithm is an effective and successful method for enhancing the reconstruction standards and performance capabilities. Additionally, the data addition procedure used in the filtered MLEM algorithm has the capability to be added consistently to the OSEM algorithm.For the reconstruction of projection data with inadequate iterations and noise, a filtered MLEM algorithm has been developed and applied. Under this specific scenario, the analytical capacity and test phantom reproduction outcomes of a Hoffman Brain Phantom illustrate that the proposed


This paper examines how haptic technology, virtual reality, and artificial intelligence help to reduce the physical contact in medical training during the COVID-19 Pandemic. Notably, any mistake made by the trainees during the education process might lead to undesired complications for the patient. Therefore, training of the medical skills to the trainees have always been a challenging issue for the expert surgeons, and this is even more challenging in pandemics. The current method of surgery training needs the novice surgeons to attend some courses, watch some procedure, and conduct their initial operations under the direct supervision of an expert surgeon. Owing to the requirement of physical contact in this method of medical training, the involved people including the novice and expert surgeons confront a potential risk of infection to the virus. This survey paper reviews recent technological breakthroughs along with new areas in which assistive technologies might provide a viable solution to reduce the physical contact in the medical institutes during the COVID-19 pandemic and similar crises. After the outbreak of COVID-19 virus in Wuhan, China at the end of 2019, this virus and its mutations has rapidly spread out in the world. In view of the fact that no proven treatment has been so far introduced for the COVID-19 patients, the prevention policies such as staying home, social distancing, avoiding physical contact, remote working, and travel restrictions has strongly been recommended by the governments. As a consequence of this global problem, universities have initiated policies regarding how to keep up teaching and learning without threatening their faculty members and students to the virus. Thus, the majority of traditional in-class courses have been substituted to the online courses. Notwithstanding the fact that the emergency shift of the classes have reduced the quality of education during the COVID-19 pandemics Nevertheless, the case of remote learning is different in the medical universities as the learning process in the medical universities is not just rely on the in-class courses. As an illustration, the medical training in the traditional way is accomplished by a medical student through attending some training courses, watching how the procedure is performed by a trainer, performing the procedure under supervision of a trainer, and at the final stage, independently performing the procedure. In fact, the traditional method of surgery training relies on excessive presence of students in the hospital environments and the skill labs to practice the tasks on the real environments such as physical phantoms, cadavers, and patients and that is why medical students are called “residents”. Thus, the aforementioned traditional surgery training methodology requires a substantial extent of physical contact between medical students, expert surgeons, nurses, and patients, and as a result, the risk of infection is high among those people. On the other hand, the assistive technologies based on virtual reality and haptic feedback have introduced alternative surgical training tools to increase the safety and efficiency of the surgical training procedures. Nowadays, the necessity of reducing the physical contact in the hospital environments seems to make another motivation for those assistive technologies. Therefore, it is beneficial to review those technologies from COVID-19 motivation aspect.In this paper, the existing assistive technologies for medical training are reviewed in a COVID-19 situation. While there are several motivations for those technologies such as increasing the safety, speed, and efficiency of training, the new motivations created for those technologies during the COVID-19 pandemic are the specific focus of this paper. In spite of the existing literature on COVID-19, our main focus is surgery training technologies that help to reduce physical contact during this and other similar pandemics. Notably, a number of those studies have analyzed systemic and structural challenges applicable to medical training programs with little emphasis on technological aspects of the subject Relevant literature was chosen from articles published by IEEE, Frontiers, Elsevier, SAGE, and Wiley with special attention to the well-known interdisciplinary journals. The search was preformed using the keywords “remote medical training,” “skill assessment in surgery,” “virtual and augmented reality for medical training,” “medical training haptic systems,” and “artificial intelligence and machine learning for medical training” until June 30, 2021. The literature was examined to systematically address key novel concepts in remote training with sufficient attention to the future direction of the subject. Finally, it is tried to review the problem in the COVID-19 context in a way that the discussed materials are distinct from similar literature in a conventional non-COVID context.The rest of this paper is organized as follows: The clinical motivations of the training tools are discussed in The process of skill development among medical students have always been a challenging issue for the medical universities, as the lack of expertise may lead to undesired complications for the patients • Eye surgery:However, the closed surgery technique is more challenging from the surgeon’s point of view since the surgeon does not have a complete and direct access on the surgical site and the tiny incisions limit the surgeon’s accessibility. Owing to the limited access, some degrees of freedom are missing and surgeon’s manipulation capability is considerably reduced. Furthermore, there is fulcrum effect at the entry point of the instrument, i.e., the motion of the tip of the instrument, which is placed inside the organ, and the external part of the instrument, which is handled by the surgeon, are reversed. This results in more difficult and even awkward instrument handling and requires specific and extensive surgical training of the surgeon. As a result, the minimal invasive surgeries demands advanced expertise level, the lack of which might cause disastrous complications for the patient. These conditions are equally important in many medical interventions, especially in minimally invasive surgeries. Here a number of specific areas of surgical operation are expressed in order to address complications that might occur during the training procedures.• Laparoscopic CholecystectomyAn important category of medical interventions which need a very high skill level is intraocular eye surgical procedures. Notably, the human eye is a delicate and highly complex organ and the required accuracy for the majority of intraocular surgeries is in the scale of 50–100 microns. The closed type of surgery is applicable to a number of eye surgeries such as the Cataract surgery in the anterior segment as well as the vitro-retinal surgical procedures in the posterior segment. Notably, some complications such as Posterior Capsule Rupture (PCR) for cataract surgery and retina puncture for the vitro-retinal surgical procedures are among the relatively frequent complications that might happen, due to the surgeon’s lack of surgical skills and dexterity. It is shown in a study on ophthalmic residents that the rate of complications such as retinal injuries is higher for the residents with less skills • NeurosurgeryAnother example is Laparoscopic Cholecystectomy (LC) which is now the accepted standard procedure across the world and is one of the most common general and specialist surgical procedures. However, it can be prone to an important complication that is bile duct injury (BDI). Although BDI is uncommon but it is one of the most serious iatrogenic surgical complications. In extreme BDI cases, a liver resection or even liver transplantation becomes necessary. BDI is considered as an expensive medical treatment and its mortality rate is as high as 21% • Orthopedic surgeryNeurosurgery is another field that deals with complex cases and requires high accuracy and ability in the surgeon’s performance. In a prospective study of 1,108 neurosurgical cases, 78.5% of errors during neurosurgery were considered preventable In the field of orthopedics, knee and shoulder arthroscopic surgeries are among the most commonly performed procedures worldwide. There is a steep learning curve associated with arthroscopic surgery for orthopaedic surgery trainees. Extensive hands-on training is typically required to develop surgical competency. The current minimum number of cases may not be sufficient to develop competency in arthroscopic surgery. It is estimated that it takes about 170 procedures before a surgeon develops consultant-level motor skills in knee arthroscopic surgery The above motivation shows the importance of skill development among the medical students. The standard process of procedural skill development in medicine and surgery is shown as a diagram in Remarkably, a learning curve is considered for each procedure, which means that performance tends to improve with experience. This concept applies for all of the medical procedures and specialties, but complex procedures, surgery in particular, are more likely to gradual learning curves, which means that improvement and expertise is achieved after longer training time. Some of the important factors in the learning curve are manual dexterity of the surgeon, the knowledge of surgical anatomy, structured training and mentoring and the nature of the procedure. The learning curve is longer for minimally invasive procedures than that for open surgical procedures. The learning curve is also influenced by the experience of the supporting surgical team. Besides, learning curves depend on the frequency of procedures performed in a specified period. Many studies suggest that complication rates are inversely proportional to the volume of surgical workload.Notably, the above mentioned process of skill development require a considerable extent of physical contact between the trainees, the expert surgeons, the nurses, and the patients, while this shall be reduced in the COVID-19 pandemic. In addition to the high risk of infection in the medical universities with the conventional medical training approaches, the majority of the health-care capacity is focused on fighting the COVID-19 virus and consequently, the education requirements of medical universities are failed to be entirely fulfilled. As a result, the training efficiency of medical universities will be reduced, provided that they just rely on the conventional training approaches. This will have possible side-effects on the future performance of the health-care system mainly due to the insufficient number of recently graduated students with adequate expertise level.On the other hand, traditional education takes place in hospitals and on real patients, which face several problems during the COVID-19 pandemic: the hospital environment is contaminated with the virus, hospital staff and physicians are very busy and tired and have less training capacity, prolonged hospital stays of patients to train students put them at greater risk for exposure to the virus, especially if complication occurs by a resident who does not have gained sufficient skills during the training procedure. Therefore, training with assistive devices outside the hospital may play an effective role in this situations. The highlighted factors can significantly be improved by assisted learning, especially in minimally invasive procedures. In more complex surgeries, the complications becomes more serious, the learning curve will be longer, and the role of assisted learning becomes more prominent.To solve the above mentioned problems, assistive training tools provide a variety of solutions through which the medical universities are able to continue their education procedures, while the risks enforced by the COVID-19 outbreak are reduced. In the following sections, the main assistive training tools including the haptic systems, virtual reality, machine vision, and data mining are reviewed and the areas in which those technologies facilitate the training process during the COVID-19 pandemic are detailed. The aim of these technologies is to have the training efficiency higher or at least equal to that of the conventional training methods without risk of infection of the involved parties to the virus.Virtual Reality is employed to create an immersive experience for various applications such as visualization, learning and education. In virtual reality, a computer generated graphical presence is visualized using a head mounted display and the user can interact with 3D objects located in the virtual world. In addition to VR, the Augmented Reality (AR) is developed to add 3D objects to the real world creating a different experience by adding digital information to the real objects in the surrounding environment. Although experiencing the 3D objects in VR scenes is far from the interaction with real objects, the VR experience is getting closer to the real world environments by the help of more realistic computer graphics and full-body haptics suits.The virtual reality (VR) and augmented reality (AR) are getting more interest as a training technique in the medical fields, unlocking significant benefits such as safety, repeatability and efficiency Some advantages of using VR/AR in medical training are depicted in VR/AR techniques are employed in various applications in surgical training as it can be seen in % while the number of collisions between arthroscope and soft-tissues decreased almost by 90%.It is shown that the learning curve of hip arthroscopy trainees is significantly improved using a virtual reality simulator . In thisVR is also employed in orthopedic surgical training, where 37 residents participated in a study to obtain an understanding of the LISS% training time reduction. Additionally, VR based surgical training helped the residents to finish glenoid exposure significantly faster than the residents trained by conventional education methods.The applicability and effectiveness of VR based training in orthopedic education is evaluated in , where nOrthognathic surgery is another surgery field considered for VR based training as it is one of the complex surgical procedures . While cIn conjunction with VR, the AR technology has also been used in various medical fields for training such as neurosurgical training . AnatomiWhile key features of VR/AR have led to improved training specially in surgical training, there are some limitations that should be considered . The firIt can be concluded that in spite of some limitations, VR/AR based simulators equipped with a haptic device can be used in medical surgery training in order to achieve skill improvement and training time reduction. Furthermore, during the isolation requirements due to COVID-19 pandemic, VR/AR based techniques can be well employed for medical training.Haptic systems provide the sense of touch with remote objects without the need of actual contact. It also provides collaboration between several operators without the need of any physical contact. As depicted in Single user haptic systems extend the abilities of human operators to interact with remote, virtual, and out-of-reach environment. In the field of surgery training, a number of investigations have proposed haptic training simulators for training of minimally invasive surgery (MIS) ® is the da Vinci Si Surgical System which supports training and collaboration during minimally invasive surgery. The da Vinci Si System builds on the existing da Vinci technology, where it has a number of enabling features such as leading-edge 3D visualization, advanced motion technology, and sufficient dexterity and workspace. However, the da Vinci Si does not provide active supervision and intervention of the trainer on the trainee’s actions. As an illustration, in the case that the trainee controls the procedure, the trainer does not have the possibility to guide the trainee during the procedure.The cooperative and joint conduction of an operation either for the purpose of collaboration or training, as a fundamental clinical task, cannot be provided by single user haptic systems. In order to make the cooperation of two surgeons possible, the system should be upgraded to a dual user haptic system by adding another haptic console. A dual user haptic system is a more recent advancement in haptic technology, and it consists of two haptic consoles, one for the trainer and one for the trainee dominance factor to determine the task dominance of each operator The issue of supervision and intervention of the trainer during the operation in dual user haptic systems have been a topic of active investigation during the past years. A number of studies have utilized the concept of Fortunately, a number of investigations have developed control architectures to address the above shortcoming of the previously proposed haptic architectures Although the above investigations address the essential theoretical aspects regarding dual user haptic systems, the commercialization of collaborative haptic system needs more attention. In the past years, some research groups have developed pilot setups of dual user haptic system with the primal clinical evaluation that have the potential of commercialization. For instance, the ARASH-ASiST system provides training and collaboration of two surgeons and it is preliminary designed for Vitreoretinal eye surgical procedures Notwithstanding the fact that teleoperated haptic systems provide key benefits for remote training during COVID-19 pandemic, they face a number of challenges that inspire perspectives of future investigations. First, the haptic modality is not sufficient to recreate the full sense of actual presence at the surgical room near an expert surgeon. To overcome this challenge and increase the operators telepresence, the haptic, visual, and auditory components are augmented to achieve a multi–modal telepresence and teleaction architecture in A vital element of a training program is how to evaluate the effectiveness of exercises by introducing a grading system based on participants’ performance. The conventional qualitative skill assessment methods require physical contact between the trainer and the trainee since they are based on direct supervision of the trainer. On the other hand, the systematic approaches for skill assessment are based on collecting the required data using appropriate instruments and analyzing the obtained data, while they eliminate the requirement of physical contact between the trainer and the trainee. Thus, reviewing the systematic data-based methods is of utmost importance, as they can be utilized to reduce the physical contact during the COVID-19 Pandemic. In this section, some of the state of the art methods in surgical skill evaluation are reviewed. Following the trend of similar research in the context of surgical skill evaluation, we categorize the reviewed methods by two criteria. The first is the type of data, and the method uses for grading the participant. The second criterion is the features extraction techniques that are used during the evaluation stage.Generally speaking, two types of data may be available in Robotic-Assisted surgery; kinematic and video data. Kinematic data is available when a robot or haptic device is involved. The most common form of capturing kinematic information is using IMUs, encoders, force sensors, magnetic field positioning sensors, etc. The video is generally recorded in all minimally invasive surgeries using endoscopy procedures.Kinematic data are more comfortable to analyze because the dimensionality of kinematic data is lower than video data. Moreover, Kinematic information is superior to video in measuring the actual 3D trajectories, and 3D velocities Other than the kinematic and video data, another source of information is often disregarded in the literature. The expert surgeon who conducts the training program can evaluate the trainee’s performance and provide useful feedback regarding his/her performance. This type of information, which is at another semantic level compared to the sensory data, is called soft data. The hard and soft information fusion methods can merge the expert’s opinion with the kinematic and video data (hard data) to accomplish a better grading system.Most surgical skill evaluation methods utilize a feature extraction technique to classify the participant’s skill level after acquiring the data, like expert, intermediate, and novice. The classification problem can be solved by employing some hand-engineered features or features that are automatically extracted from the data. Hand-engineered features are interpretable and easy to obtain. However, hand-engineered features are hard to define. Specifically, defining a feature that represents the skill level regardless of the task is not trivial. Therefore, the states of the art methods are commonly based on automatic feature extraction techniques. An end-to-end deep neural network is used to unfold the input data’s spatial and temporal features and classify the participant in one of the mentioned skill levels in an automated feature extraction procedure. While, The most convenient hand-engineered features are those introduced by descriptive statistics Since many surgical tasks are periodic by nature, the data frequency domain analysis proves to be effective As it is mentioned before, hand-engineered features are task-specific. For example, the frequency domain analysis discussed in the previous section is only viable when the task is periodic. Otherwise, the frequency domain features should be concatenated with other features. Moreover, perceiving the correct features that reflect participants’ skill levels in different surgical tasks requires an intensive knowledge of the field. As a result, developing a method in which the essential features are identified automatically is advantageous.With the recent success of Convolutional Neural Networks (CNN) in classification problems like image classification, action recognition, and segmentation, it is safe to assume that CNN can be used in skill assessment problems. However, unlike image classification, improvement brought by end-to-end deep CNN remains limited compared to hand-engineered features for action recognition To the best of our knowledge, all of the proposed methods in the literature have used single classifier techniques in their work. However, methods like classifier fusion have proved to be useful in the case of medical-related data. In In conclusion, the general framework illustrated in The introduction of new hardware capable of running deep learning methods with acceptable performance led artificial intelligence to play a more significant role in any intelligent system Not needing a special preprocessing, deep convolutional neural networks (CNNs) are commonly used for classifying images into various distinct categories. For instance, in medical images, this may include probable lesions k proposal regions, while a classifier may be employed for the later stage. Dealing with some challenging problems in these detectors, There are numerous researches carried out in the field of image processing based on CNNs. These methods are mainly divided into two single-stage and two-stage detectors. The former is known to be fast while the latter results in higher accuracy. In To put all in a nutshell, when dealing with critical situations such as the current COVID-19 epidemic, it is highly recommended to employ artificial intelligence techniques in image processing namely deep CNNs for medical training tasks. By this means, neither is a close physical interaction between the expert and novice necessary, nor the quality of the training is reduced adversely due to the limitations. In fact, the computer vision approach acts as an interface making it possible both to learn from the expert and to evaluate the novice, remotely.The faculty members and the students of the medical universities are classified in the high-risk category due to the potential exposure to coronavirus through direct contact and aerosol-generating procedures. As a result, many medical schools have suspended their clinical programs or implemented social distancing in their laboratory practices. Furthermore, the current fight against the COVID-19 virus have used nearly all capacity of health-care systems, and some less urgent and less emergent medical services including the education issues are limited or even paused. Therefore, unless some assistive training tools are utilized to support the educational procedures, the training efficiency of medical universities will be reduced and it have future consequences for the world health-care system.Practicing medical tasks with current lock-down policies can be solved utilizing state of the art techniques in haptics, virtual reality, machine vision, and machine learning. Notably, utilization of the above technologies in medical education has been researched actively within the past years in order to increase the safety and efficiency of the surgical training procedures. Nowadays, another motivation is created for those assistive technologies owing to the COVID-19 pandemic. In this paper, the existing assistive technologies for medical training are reviewed in the COVID-19 context and a summary of them is presented in It is reviewed that a surgical simulator system including a VR/AR based graphical interface and a haptic interface is able to provide the circumstances of actual surgical operation for the medical students, without the necessity of attending the hospital environments. Furthermore, through augmenting the system with another haptic console and having a dual user haptic system, the opportunity of collaboration with and receiving guidance cues from an expert surgeon in a systematic manner is given to the trainees. In contrast to the traditional collaboration methodologies, the haptic-based collaboration does not require the physical contact between the involved people and the risk of infection is reduced. Assessment of the expertise level of the medical students is another element of each training program. The necessity of reducing physical contact during the COVID-19 pandemic have also affected the skill assessment methodologies as the traditional ways of skill assessment are based on direct observation by a trainer. In contrast, data-based analysis may be utilized as a systematic approach for skill assessment without any need of physical contact. In this paper, some of the ongoing methods in surgical skill evaluation have been reviewed.Biomedical engineering technology has progressed by leaps and bounds during the past several decades and advancements in remote diagnostics and remote treatment have been considered as a leading edge in this field. For instance, the tele-surgery robotic-assisted da Vinci system have received a great deal of attention in the healthcare marketplace with more than 5 million surgeries in the last 2 decades


This work illustrates the analysis of Film Bulk Acoustic Resonators (FBAR) using 3D Finite Element (FEM) simulations with the software OnScale in order to predict and improve resonator performance and quality before manufacturing. This kind of analysis minimizes manufacturing cycles by reducing design time with 3D simulations running on High-Performance Computing (HPC) cloud services. It also enables the identification of manufacturing effects on device performance. The simulation results are compared and validated with a manufactured FBAR device, previously reported, to further highlight the usefulness and advantages of the 3D simulations-based design process. In the 3D simulation results, some analysis challenges, like boundary condition definitions, mesh tuning, loss source tracing, and device quality estimations, were studied. Hence, it is possible to highlight that modern FEM solvers, like OnScale enable unprecedented FBAR analysis and design optimization. Acoustic Wave Resonators (AWR) have been studied starting around 50 years ago. These devices promise to improve communication platforms, sensing devices, and many other systems. AWRs are currently used in a variety of applications as Radio Frequency (RF) filters , gravimeThe main types of AWRs are Surface Acoustic Wave (SAW) and Bulk Acoustic Wave (BAW) resonators. They differ from each other depending on the type of waves that support resonance and its propagation from the source through or over the material . BAW devThe main contribution of this paper is the illustration of the FEM analysis process applied to a realistic FBAR design previously reported in Reference . The anaThis work is organized as follows: According to the first law of thermodynamics, energy cannot be created or destroyed, but it can be transformed from one form to another. Using piezoelectric materials, mechanical energy can be transformed into electrical (direct effect), or vice versa (inverse effect) . IdeallyThe design of an AWR define its type of resonator. Basically, there are two general types: Bulk Acoustic Wave (BAW) and Surface Acoustic Wave (SAW) resonators. In general, the resonance frequency is directly proportional to the wavelength (λ) . For acoFor an FBAR design, a cavity under the device to confine the energy is required. The fabrication of this cavity presents technical challenges. For instance, the process needs to deal with high temperatures and minimize the roughness of the deposited layer or its piezoelectric properties that ultimately limit the resonator performance.Therefore, a design of experiments study is required to determine the manufacturing conditions that result in the best resonator performance. Resonator performance is limited by material and geometric factors, among them the quality of the piezoelectric and metal layers, as well as the geometric fidelity of the cavity, anchors, electrodes, and the rest of the structure.Useful comparison of resonator devices can be done by comparing performance metrics, like quality, coupling coefficient, and the Figure of Merit (FoM).These quantities can be derived from the Butterworth Van-Dyke (BVD) equivalent circuit models ,15. The quencies ,17:(1)fr represents the ratio of stored energy and energy lost on different loss mechanisms. It can be computed from the BVD circuit elements as Qf=2πIn turn, the Coupling coefficient Equation :(4)keff2Finally, the Figure of Merit is a useful quantity for comparisons that captures both the quality and coupling coefficient, from Equations  and 4),,4), as sMultiple techniques can be implemented to extract BVD models based on FEM analysis as presented in Reference .This parTwo design considerations are critical to manufacturing a resonator: the control of the manufacturing process and the knowledge of the set of parameters achieving the expected performance ,20. The While the design process of other microelectromechanical systems (MEMS) devices, like capacitive sensors ,22,23, cx, y and z directions generated by these defects. Consequently, in many cases, it is necessary to run several fabrication cycles to arrive at a final design in what is referred to as Device Validation in Current and conventional methodology for AWR design based on 2D simulation is shown in In contrast to 2D simulation models, 3D simulation models capture device complexities more accurately. For example, they can capture electrode, piezoelectric layer, and cavity shapes, pads location, anchor losses, fabrication defects, etc. Nevertheless, there still exist challenges that limit the level of representation accuracy of modern 3D FEM simulation tools available today. This section outlines some of those challenges and presents a review of typical design process based on 3D simulations ,28.Finite Element Method equations must be solved at each node of the complete meshed model . MoreoveIt should be noted that the size of each element and the number of elements are defined by Equations  and 7),,7), respFacing these challenges, OnScale’s solvers were designed with Message Passing Interface (MPI) routines in mind, achieving unparalleled acceleration on Cloud HPC, and time domain computation to be post-processed using techniques as Fourier transform. In addition, the software performs temporal sub-sampling that can be carried out without causing aliasing of the data (fulfilling the Nyquist frequency), and then, the Fourier updates are not calculated at each time-step. All these capabilities allow to reduce the computational overhead .The commercial software OnScale that implements a parallel and efficient FEM algorithm is used in this work. It is capable of solving Multi-Billion element models requiring about 1GB of RAM per 10–15 billion elements. Furthermore, the availability of an HPC cloud service from the same company allows running simulations in only few hours, and, as a consequence, it enabled the studies presented in this paper.The methodology shown in 1.Main Specifications: First, the design specifications must be clearly defined. It is suitable to define criteria that can be modified to make decisions in the next steps of this methodology, as well as the tolerable range for each parameter in the design 2.2D model: Two-dimensional modeling allows to carry out a harmonic analysis in order to know the main resonance modes. In this stage, the materials of the device, their thickness and their boundary conditions are defined. The main criterion to go to the next step is the match between the resonance and anti-resonance frequencies of the model with the specifications defined in Step 1.3.3D model: The input for 3D modeling will be the configuration of materials and thicknesses established in Step 2: however, in the 3D simulation, the effects of the density of the materials, the shape of the electrodes, the active area, and the effect of anchors, among others, will be observed. It is very important to consider an optimized mesh resulting of the mesh study. Boundary conditions play a very important role in the simulation; for this reason, they must be well defined and located. The main criteria to go to Step 4 is the value of the mechanical coupling and the quality factor of the device. Their values will be defined by the analysis of the effects of geometry, material properties, and boundary conditions. The values of these parameters should be correlated with the tolerance ranges defined in Step 1.4.Device Manufacturing The manufacture of the device will be carried out when a functional design is obtained in 3D simulations as expected. This is where a feedback between device manufacturing constraints and 3D modeling should be done to consider any possible effect of the manufacturing constraints. At this stage, it is convenient to define the feasibility of the prototype designed in 3D and simulate as many times as necessary the thicknesses achievable by the available deposition equipment resolution and all the modifications that the manufacturing tolerances impose on the design.5.Device Validation: After the manufacturing process, the feedback given to the 3D simulation is very important, correcting the dielectric, acoustic or structural losses necessary so that the response in the simulation is as close as possible to the measurement of the device. In this way, the effects of the process are captured in the 3D modeling. This will allow the following cycles to consider these effects so that the designer can apply design techniques to reduce losses or suppress spurious resonances. The validation of the model will be carried out by comparing the specifications given in the first step with the result of the manufactured device. If the response of the device is far from what is expected, the cycle repeats.The methodology consists of the following steps, as shown in Moreover, The FBAR resonator presented in Reference  was chosTwo-dimensional simulations of manufactured were run in OnScale. Results presented in The 3D model was reduced to a 25% of the FBAR physical size, by applying symmetry conditions to the eference . MateriaAlthough the simulation could be run in the cloud with less restrictions of the available hardware, it is well known that saving computing resources is essential. For this reason, most designs will require a mesh study that will minimize the amount of computing resources needed to solve the model."keypoints" (forced mesh lines that help to define the geometry layers or boundaries). There are multiple options to configure the mesh in OnScale. For example, this can be done by defining the elements per wavelength (EPW) value, the minimum mesh size or the number of elements per axis, per layer, or per section.Moreover, meshing the model is a complex task for a 3D simulation. OnScale offers a solution for this problem. As default, it meshes the model with a uniformly distributed squared grid. While, for other simulators, the mesh is adapted to the geometry, in OnScale, the model is adapted to the mesh defined. Thus, the importance of defining properly the Mesh size can be analyzed one axis at a time . An example of mesh size study is presented in It is well known that different kind of losses affect the FBAR performance, and they can be analyzed in FEM simulators. The following energy balance equation can be found in Reference :(8)EIN=EHowever, viscoelastic losses did show some impact. This prompted a more careful analysis of this type of loss. For the analysis of viscoelastic losses the viscoelastic constant available from measured data may not be adequate because it is obtained from measurements of macroscopic phenomena . In addiIn Onscale, there are multiple mathematical models available to simulate viscoelastic effects: Newtonian Viscosity (nwtn), Stiffness Proportional Damping (sdmp), Rayleigh Damping (rdmp), Viscous Damping (vdmp), and Mass Proportional Damping (mdmp)., dB loss , Quality factor , Log decrements, or Fraction of critical damping D.Damping estimations can be done by describing the wave amplitude or energy and whether the loss is per cycle or per unit distance: Attenuation coeficient The Rayleigh Damping (rdmp) model was chosen to develop the simulation tests presented in this work. It combines mdmp and sdmp.c is the phase velocity, and p) and a shear wave (s), respectively, are shown in Equations ,QmThe first set of simulations to analyze viscoelastic losses was focused on a sweep study of the quations .The second set of simulations to analyze viscoelastic losses was focused on a sweep study of the For the third case, the sweep study was done for the  quality . As repo quality  for thisTo summarize the above analysis, the Quality Factor quations –5). Thi. ThiQf oThe piezoelectric material deposition process is a complex task since it should have crystalline characteristics, its surface also requires to be as regular as possible and its properties should be preserved as much as equipment and environment conditions allow. A complete research study of piezoelectric characteristics of ZnO films grown by RF magnetron sputtering can be found in Reference , experimAlthough the manufacturing effects can not be directly configured in 3D simulations, this study shows the possibility to predict their impacts through the viscoelasticity variable configurations.To validate the proposed methodology, the value of eference .A methodology for extraction of Butterworth-Van Dyke model (BVD) parameters of FBAR was programmed in MATLAB. The FBAR model frequency characteristic is obtained through FEM simulations or measurements. Then, the BVD model parameters are calculated and further optimized to fit the frequency response to those from FEM analysis and measured signal.The circuit parameters of both BVD models are extracted from the impedance response derived from simulations and measurements, respectively, quality factor and figure of merit was calculated using –5)..5).Hence, two BVD models were developed corresponding with the simulations of this paper and the measurements of Reference . The BVDeference . As can The simulation process described in The As mentioned above, regardless of the increasing power of solvers, like OnScale, it is still important to take advantage of all possibilities to reduce the computational complexity of the model. These include, for example, exploiting symmetry and impedance conditions, and running comprehensive mesh refinement studies prior to model optimization. OnScale also provides scripting tools, like Analyst Mode, to be able to efficiently manipulate the mesh size in all directions by layer or by section.Additionally, it can be highlighted that the kind of analysis presented here is challenging with traditional means of calculating acoustic devices. Most of the 3D models presented in this work had more than 500 million degrees of freedom. This makes it impossible to solve in a conventional way. Three-dimensional simulations for viscoelastic losses in piezoelectric layer were run in the cloud with thousands of millions of elements. In contrast, 3D simulations for viscoelastic losses in electrodes layer were run in a local machine with hundreds million elements and results were obtained in 2–3 h. The Hardware used for local simulations was a PC with processor Intel Xeon (R)W-2135, Nvidia Quadro P1000, and 64 GB RAM memory.Lastly, it is important to also consider at least one of the economic implications of the advent of powerful FEM solvers. The capability of solvers, like OnScale, to distribute the computation task on multiple servers on the cloud implies that companies developing AWR devices and technologies now have the option to reduce the amount of capital investment on HPC infrastructure. This investment includes not only the cost of the equipment but its maintenance, depreciation, and all the costs related to the required facilities . This may be specially important for companies, large and small, pondering the risk of AWR project development with the required significant initial investments in HPC infrastructureIn summary, the following advantages and disadvantages of the type of analysis presented in this paper may be highlighted:Mesh size optimization: Computation time can be reduced Better capture of Manufacturing aspects: Fabrication process is a hard task when acoustic resonators are being manufactured. Fabrication defects can be included as part of the simulation in order to approach the final device before manufacturing.Broadband capability: Time domain solvers allow simulations to represent a broad spectrum of frequencies in the order of GHz. This allows capturing not only the main resonance frequency but a range of spurious responses, as well.Analysis flexibility in combination with post-processing tools: powerful tools, like OnScale, enable interfacing with general purpose numeric environments, like MATLAB. This introduces a level of flexibility to enable post-processing steps, like the ones demonstrated in this paper, related with dielectric loss analysis.Easier tool adoption: FEM solvers running on the cloud provide an alternative to the initial capital investment and the related costs of maintaining an HPC infrastructure.Advantages:Learning Curve: The powerful capabilities of modern FEM solvers, like OnScale, demand learning new information about the use of the software tool. Despite the advantages of a versatile Graphical User Interface (GUI), many commands and options must be learned. Additionally, some tasks may only be configured using a lower level scripting interface mode  that also require a learning curve.Frequency resolution: Common to all FEM time domain solvers, the broadband capability mentioned in the advantages introduces a trade-off in frequency resolution. Fine frequency resolution imply a larger time analysis that may potentially override the bandwidth advantage.Cost: Despite the advantage of leveraging the capital investments and cost of procuring and maintaining HPC infrastructure, the cost of the cloud services are still of critical consideration. Cloud services are charged by the core-hour that is a measure of the level of computing resources required. Depending on the complexity of the model, the analysis may require in the hundreds or thousands of core-hours even if following the mentioned methods to reduce computation requirements. This also increases the cost of modeling or analysis setup errors. Modeling or analysis setups that end-up in invalid simulation results also bear the consequent monetary costs of wasted cloud service time.Disadvantages:In contrast to 2D simulation models, 3D simulation models capture device complexities more accurately as shown in AWRs had not benefited from 3D simulations due to higher computational requirements, manufacturing complexity and lack of validation between measurements and 3D simulation results. The novelty of the presented work was a design methodology including a stage of OnScale 3D simulations and its validation in a fabricated model, previously reported in the literature. In the study case, the small error found in The advent of powerful 3D FEM simulators, like OnScale, opens multiple possibilities to accelerate and optimize the FBAR design process. This paper illustrates the analsysis of an FBAR design based on 3D FEM simulations using OnScale. The analysis highlights the possibility of device design optimization considering the impact from fabrication process aspects, like the quality of material deposition. The analysis presented in this paper was performed using OnScale’s HPC cloud service. This enabled solution times in the order of hours instead of days. Hence, it becomes possible to run detailed studies, like a mesh study to ensure proper device numerical representation or configure the timestep to properly capture the model behavior in the steady-state.The analysis was performed on the FBAR device reported in Reference . The anaThese kind of analysis also enable fabrication process feedback, since the consideration of poor quality material deposition or temperature processes effects will provide more reliability in the simulation results. Some of these effects were taken into account by configuring losses in the simulation model.The application of this methodology can be extended to the AWR design process to obtain improved performance devices in less time and using less resources. It will also give the chance to apply optimization techniques to the device before fabrication and will accelerate the research stage on novel resonator designs that minimize losses and provide spurious resonance suppression. The proposal suggests that the results can be used in other applications of the AWR design and other areas of multiphysics systems. The analysis of FBARs with complex or unsymmetrical geometries will be studied. The extension to other types of AWRs and multiple divices effects in the same die also will be considered in a future work.