Adaptive Motion Compensated Spatial Temporal Filter of Colonoscopy Video

Colonoscopy is a popular procedure which is used to detect an abnormality. Early diagnosis can help to heal many patients. The purpose of this paper is removing/reducing some artifacts to improve the visual quality of colonoscopy videos to provide better information for physicians. This work complements a series of work consisting of three previously published papers. In this paper, optic flow is used for motion compensation, where a number of consecutive images are registered to integrate some information to create a new image that has/reveals more information than the original one. Colon images were classified into informative and noninformative images by using a deep neural network. Then, two different strategies were used to treat informative and noninformative images. Informative images were treated by using Lucas Kanade with an adaptive temporal mean/median filter, whereas noninformative images were treated by using Lucas Kanade with a derivative of Gaussian (LKDOG) and adaptive temporal median images. Comparison showed that this work achieved better results than those achieved by the state-of-the-art strategies for the same degraded colon images data set. The new proposed algorithm reduced the error alignment by a factor of about 0.3, with a 100% successful image alignment ratio. In conclusion, this algorithm achieved better results than the state-of-the-art approaches in case of enhancing the informative images as shown in the results section; also, it helped to reveal some information from noninformative images that have very few details/no details.

Apical Extrusion of Debris during Root Canal Preparation with ProTaper Next, WaveOne Gold and Twisted Files

(1) Background: Apical extrusion of debris is an example of a complication that may arise during root canal treatment, and it has been proven to be an unavoidable occurrence during endodontic treatment by numerous authors. Even though it may not hinder the long-term outcome of treatment, it may lead directly to increased levels of postoperative pain and, therefore, lower levels of patient acceptance and satisfaction. The aim of the study was to assess the weight of apically extruded debris during root canal preparation with instruments that use different movement kinematics (rotary, reciprocating, and adaptive motion); (2) Methods: The study was performed using the Myers and Montgomery model. Sixty human premolar teeth were inserted into preweighed Eppendorf tubes and randomly classified into three groups. After manual glide-path preparation, teeth in each group were instrumented to working length set 1 mm short of the anatomical apex using the standard sequence provided by the manufacturers (for Group 1: ProTaper Next X1 & X2; for Group 2: WaveOne Gold Primary, for Group 3: Twisted Files SM1-SM3). Root canals were irrigated with 1 mL of 0.9% NaCl solution between each file insertion. The tubes with collected debris were stored in an incubator at 70 °C for 5 days in order to evaporate the liquid component. Measurement of the weight of extruded debris was performed by subtracting the preinstrumentation from the postinstrumentation weight of the tubes. The results were analyzed with Kruskal–Wallis ANOVA, with significance level set at 0.05; (3) Results: The weight of extruded debris was 0.337 mg (SD = 0.148) for Group 1, 0.305 mg (SD = 0.201) for Group 2, and 0.348 mg (SD = 0.135) for Group 3. (4) Conclusions: Engine-driven root canal preparation with the use of instruments ProTaper Next, WaveOne Gold and Twisted Files that use different movement kinematics (rotary, reciprocating, and adaptive motion) was associated with apical extrusion of debris to a similar extent.

Adaptive control for networked uncertain cooperative dual-arm manipulators: an event-triggered approach

In this paper, an improved adaptive motion-force control approach is introduced to control the cooperative manipulators transporting a shared object under limited communication. The adaptive controller is designed based on the backstepping approach to control the motion of the handled object in the presence of uncertainties and external disturbances. Moreover, the force controller is established to maintain constant internal forces. An event-triggered (ET) mechanism is derived based on the Lyapunov analysis to deal with the bandwidth restrictions and maintain the system stability during the cooperative manipulation. The effectiveness of the proposed control scheme is investigated by comparing it with the existing variations of adaptive backstepping control (i.e., traditional and state augmented schemes). Moreover, the designed triggering mechanism is compared with different triggering conditions presented in the literature. The proposed control approach is further validated in a more realistic virtual robot experimentation platform (i.e., V-REP) using two SCORBOT-ER VPlus manipulators. From the TrueTime-based simulation runs, the proposed control scheme exhibits superior performance in tandem with efficient utilization of the network resources during the transportation task.

Human Action Recognition Using Spatio-Temporal Multiplier Network and Attentive Correlated Temporal Feature

Nowadays, action recognition has gained more attention from the computer vision community. Normally for recognizing human actions, spatial and temporal features are extracted. Two-stream convolutional neural network is used commonly for human action recognition in videos. In this paper, Adaptive motion Attentive Correlated Temporal Feature (ACTF) is used for temporal feature extractor. The temporal average pooling in inter-frame is used for extracting the inter-frame regional correlation feature and mean feature. This proposed method has better accuracy of 96.9% for UCF101 and 74.6% for HMDB51 datasets, respectively, which are higher than the other state-of-the-art methods.

An adaptive motion planning algorithm for obstacle avoidance in autonomous vehicle parking

In the recent era, machine learning-based autonomous vehicle parking and obstacle avoidance navigation have drawn increased attention. An intelligent design is needed to solve the autonomous vehicles related problems. Presently, autonomous parking systems follow path planning techniques that generally do not possess a quality and a skill of natural adapting behavior of a human. Most of these designs are built on pre-defined and fixed criteria. It needs to be adaptive with respect to the vehicle dynamics. A novel adaptive motion planning algorithm is proposed in this paper that incorporates obstacle avoidance capability into a standalone parking controller that is kept adaptive to vehicle dimensions to provide human-like intelligence for parking problems. This model utilizes fuzzy membership thresholds concerning vehicle dimensions and vehicle localization to enhance the vehicle’s trajectory during parking when taking into consideration obstacles. It is generalized for all segments of cars, and simulation results prove the proposed algorithm’s effectiveness.

A New Shape Adaptive Motion Control System

In recent years there is a growing trend on integrating Computer Aid Design (CAD), Computer Aid Manufacturing (CAM) and Computer Aided Inspection (CAI). This thesis presents a new shape adaptive motion control system that integrates part measurement with motion control. The proposed system consists of five blocks: surface measurement; surface reconstruction; tool trajectory planning; axis motion control and part alignment In this thesis, the key technology used in surface measurement and surface reconstruction is spatial spectral analysis. In the surface measurement block, a new special spectrum comparison method is proposed to find out an optimal digitizing frequency. In the surface reconstruction block, different interpolation methods are compared in the spatial spectral domain. A spatial spectral B-Spline method is presented. In the tool trajectory planning block, a method is developed to select the motion profile first and then determine the tool locations according to the reconstructed surface in order to improve the accuracy of the planned path. In the part alignment, a three-point alignment method is presented to align the part coordinates with the machine coordinates. Based on the proposed methods, a software package is developed and implemented on the polishing robot constructed at Ryerson University. The effectiveness of the proposed system has been demonstrated by the experiment on edge polishing. In this experiment, the shape of the part edges is measured first, and then constructed as a wire-frame CAD model, based on which tool trajectory is planned to control the tool to polish the edges.